Supplies a set of functions to query air travel data for user- specified years and airports. Datasets include on-time flights, airlines, airports, planes, and weather.

This package provides a decision support tool to strategically prioritise evidence gathering in complex, hierarchical AND-OR decision trees. It is designed for situations with incomplete or uncertain information where the goal is to reach a confident conclusion as efficiently as possible (responding to the minimum number of questions, and only spending resources on generating improved evidence when it is of significant value to the final decision). The framework excels in complex analyses with multiple potential successful pathways to a conclusion ('OR nodes). Key features include a dynamic influence index to guide users to the most impactful question, a system for propagating answers and semi-quantitative confidence scores (0-5) up the tree, and post-conclusion guidance to identify the best actions to increase the final confidence. These components are brought together in an interactive command-line workflow that guides the analysis from start to finish.

This package provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.

This package performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) <doi:10.4025/actasciagron.v47i1.73889>.

This package provides a tool for generating acronyms and initialisms from arbitrary text input.

Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations.

An iterative process that optimizes a function by alternately performing restricted optimization over parameter subsets. Instead of joint optimization, it breaks the optimization problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.

NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.

Data sets and examples from National Health and Nutritional Examination Survey (NHANES).

This package provides functions to conduct title and abstract screening in systematic reviews using large language models, such as the Generative Pre-trained Transformer (GPT) models from OpenAI <https://platform.openai.com/>. These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) <DOI:10.1037/met0000769>.

Visualization of antibody titer scores is valuable for examination of vaccination effects. AntibodyTiters visualizes antibody titers of all or selected patients. This package also produces empty excel files in a specified format, in which users can fill in experimental data for visualization. Excel files with toy data can also be produced, so that users can see how it is visualized before obtaining real data. The data should contain titer scores at pre-vaccination, after-1st shot, after-2nd shot, and at least one additional sampling points. Patients with missing values can be included. The first two sampling points (pre-vaccination and after-1st shot) will be plotted discretely, whereas those following will be plotted on a continuous time scale that starts from the day of second shot. Half-life of titer can also be calculated for each pair of sampling points.

Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.

Datasets to Accompany S. Weisberg (2014), "Applied Linear Regression," 4th edition. Many data files in this package are included in the alr3 package as well, so only one of them should be used.

Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.

Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides a novel algorithm for solving the sparse principal component analysis problem which provides advantages over existing methods in terms of efficiency and convergence guarantees. Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>. Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>. Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.

This package implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression and machine learning as introduced in Stone and Gosling (2025) <doi:10.1080/10618600.2024.2414104>. This package provides a flexible alternative to BART (Bayesian Additive Regression Trees) using Voronoi tessellations instead of trees. Users can fit Bayesian regression models, estimate posterior distributions, and visualise the resulting tessellations. It is particularly useful for spatial data analysis, machine learning regression, complex function approximation and Bayesian modeling where the underlying structure is unknown. The method is well-suited to capturing spatial patterns and non-linear relationships.

This package provides the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x> Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300". For researchers interested in using the exact mathematical formulas and procedures as used in the original paper.

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.

Efficient algorithms <https://jmlr.org/papers/v24/21-0751.html> for computing Area Under Minimum, directional derivatives, and line search optimization of a linear model, with objective defined as either max Area Under the Curve or min Area Under Minimum.

Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform ANOVA with post-hoc tests (e.g. Tukey HSD and Duncan MRR), compute coefficients of variation, and generate publication-ready summaries. High-level wrappers allow automated multi-variable analysis with optional clustering by experimental factors, as well as direct export of results to Excel spreadsheets and high-resolution image tables for reporting. Functions build on ggplot2', stats', and related packages and follow methods widely used in agronomy (field trials and plant breeding). Key references include Tukey (1949) <doi:10.2307/3001913>, Duncan (1955) <doi:10.2307/3001478>, and Cohen (1988, ISBN:9781138892899); see also agricolae <https://CRAN.R-project.org/package=agricolae> and Wickham (2016, ISBN:9783319242750> for ggplot2'. Versión en español: Conjunto de herramientas para el análisis estadà stico, visualización y generación de reportes en ensayos agroindustriales y agrà colas. Incluye funciones para ANOVA con pruebas post-hoc, resúmenes automáticos multivariables con o sin agrupamiento por factores, y exportación directa de resultados a Excel e imágenes de alta resolución para informes técnicos.

Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.

Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.

An interactive shiny application for performing non-compartmental analysis (NCA) on pre-clinical and clinical pharmacokinetic data. The package builds on PKNCA for core estimators and provides interactive visualizations, CDISC outputs ('ADNCA', PP', ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic Noncompartmental Analysis: The PKNCA R Package" <doi:10.1007/s10928-015-9432-2>.